Storing facial recognition elements as layers to blend facial changes

ABSTRACT

A computer readable medium comprising code that causes one or more processors to: receive a first representation of a face generated from a first image of a user and receive a difference model, the difference model indicative of a difference between the first image and a second image of the user; capture a third image of the user and generate a second representation of the face of the user using the third image; determine a first likelihood of a match between the first and second representations and/or determine a second likelihood of a match between the second representation and a third representation, the third representation being a combination of the first representation and the difference model; and permit access by the user to a secure asset in instances where at least one of the first or second likelihoods of a match meets a threshold.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims priority to U.S. Provisional Pat. Appl. No. 63/132,368, titled “Storing Facial Recognition Elements as Layers to Blend Facial Changes,” filed Dec. 30, 2020, which is hereby incorporated by reference herein in its entirety.

TECHNICAL FIELD

Embodiments described herein generally relate to facial recognition in an access control system (ACS).

BACKGROUND

Facial recognition systems were once beyond the computational power of most computers. Moreover, until relatively recently, basic algorithms to solve the problem had not yet been developed. Deep neural networks have become fairly commonplace, allowing, for example, facial recognition systems able to identify a single person out of more than ten million people in less than a second. While the systems remain quite complex, they have become quite mainstream. For example, 1:1 matching is being performed on even personal mobile devices for unlocking the devices.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. Some embodiments are illustrated by way of example, and not limitation, in the figures of the accompanying drawings in which:

FIG. 1 illustrates a front view of an example ACS, or portions thereof;

FIG. 2 illustrates a top cross-sectional view of an example ACS, or portions thereof;

FIG. 3 illustrates a block diagram schematic of various components of an example ACS reader;

FIG. 4 illustrates a block diagram schematic of various example components of an example machine that may be used as, for example, an ACS control panel or ACS host server; and

FIG. 5 is a flow diagram of a method for authenticating a user in an ACS.

DETAILED DESCRIPTION

The present disclosure generally relates to facial recognition in access control systems. In general, access control covers a range of systems and methods to govern access, for example by people, to secure areas or secure assets. Physical access control includes identification of authorized users or devices (e.g., vehicles, drones, etc.) and actuation of a gate, door, or other mechanism used to secure an area or actuation of a control mechanism, e.g., a physical or electronic/software control mechanism, permitting access to a secure physical asset, such as but not limited to a computing device (e.g., desktop computer, mobile device, wearable device, copier/printer, and the like). Logical access control includes identification of authorized users or devices to provide access to logical assets, such as but not limited to, an application, a cloud-based service, or a financial or personal account. Physical access control systems (PACS) and logical access control systems (LACS) can generally include a reader (e.g., an online or offline reader) that holds authorization data and can be capable of determining whether credentials (e.g., from credential or key devices such as radio frequency identification (RFID) chips in cards, fobs, magnetic stripe cards, or personal electronic devices such as mobile phones) are authorized for accessing the secure area or asset. Alternatively, PACS/LACS can include a host server to which readers are operably connected (e.g., via a controller) in a centrally managed configuration. In centrally managed configurations, readers can obtain credentials from credential or key devices and pass those credentials to the PACS/LACS host server. The host server can then determine whether the credentials authorize access to the secure area or secure asset and command the actuator or other control mechanism accordingly or can command the reader to operate the actuator or other control mechanism accordingly.

Wireless PACS/LACS, e.g., those that utilize wireless communication between the reader and the credential or key device, such as for secure credential exchange, can use RFID or personal area network (PAN) technologies, such as the IEEE 802.15.1, Bluetooth, Bluetooth Low Energy (BLE), near field communications (NFC), ZigBee, GSM, CDMA, Wi-Fi, ultrawide band (UWB), etc. PACS/LACS may additionally or alternatively include facial recognition capabilities and use facial recognition as a sole, primary (e.g., main or first authentication factor of two or more authentication factors), or secondary authentication factor (e.g., authentication factor that is in addition to or secondary to a primary authentication factor).

FIGS. 1 and 2 illustrate an example access control system (ACS) 100, or portions thereof. While FIGS. 1 and 2 primarily illustrate a PACS, it is recognized that the present disclosure similarly relates to LACS, and that while the secure asset in FIGS. 1 and 2 is illustrated as a secure area surrounded by a wall and protected by a physical access point (e.g., a door) and the control mechanism is described as a locking mechanism, the secure asset could instead be a logical asset (e.g., an application, a cloud-based service, or a financial or personal account), the control mechanism could be an electronic/software control mechanism separate from or incorporated with the reader device, and the reader device need not be fixed and could include a device owned or operated by the user, such as a mobile device (e.g., smart phone, tablet, or the like).

ACS 100 can include a reader device, or simply reader, 102 associated with a secure area, access point, or other asset 104. In some cases, such as in the example illustrated in FIG. 1, secure asset 104 is a secure area secured by an access point 105, such as a door, gate, turnstile or the like controlling or permitting authorized access to the secure area, but as explained above, secure asset 104 may alternatively be a logical asset. Reader 102 can include or be operably connected with a control mechanism 106, such as but not limited to a locking mechanism in the case of PACS or an electronic/software control mechanism in the case of LACS, that controls whether access via access point 105 is permitted (e.g., can be opened or accessed) or may even control opening and/or closing of the access point. Reader 102 can be an offline reader, e.g., a reader not connected to a control panel or host server, and in such cases may make its own access control determinations and directly operate or command control mechanism 106, accordingly. Reader 102 can be a wireless reader device, in that the reader may communicate with credential or key devices via wireless technologies, such as RFID or PAN technologies, such as the IEEE 802.15.1, Bluetooth, Bluetooth Low Energy (BLE), near field communications (NFC), ZigBee, GSM, CDMA, Wi-Fi, UWB, etc. Reader 102 may also include a PIN pad, touch screen, fingerprint reader, magnetic stripe reader, chip reader, or other non-wireless input means for receiving credential or other information, such as a PIN or other secret code, biometric information such as a fingerprint, or information from a magnetic stripe card or chip card, for example.

Reader 102 may also include facial recognition capabilities. For example, reader 102 may include a facial recognition module 103 or otherwise integrate facial recognition components within the reader. Facial recognition module 103 may include one or more cameras or other optical sensors for capturing or receiving one or more images, such as one or more images of a user 101. Facial recognition module 103 may also include one or more processors and memory for performing facial recognition or facial verification using the captured or received images. Facial recognition module 103 may alternatively or additionally utilize one or more processors and/or memory of the reader 102. According to a first method, facial verification computes a one-to-one similarity between a probe image (e.g., image of the user's 101 face) or other representation of the probe image (e.g., template or feature vector as described further below) and each of one or more images or other representations of images (e.g., templates or feature vectors) selected from a gallery of images/templates to determine whether the probe image or template is, or the likelihood the probe image or template is, for the same subject as one or more of the gallery images or templates. Such may be referred to herein as a one-to-many facial verification. According to another method, facial verification computes a one-to-one similarity between a probe image (e.g., image of the user's 101 face) or other representation of the probe image (e.g., template or feature vector) and an image or other representation of an image (e.g., template or feature vector) previously stored (e.g., based on a previously enrolled image of the user's 101 face). Such may be referred to herein, generally, as a one-to-one facial verification. Facial verification need not be carried out on, for example, a pixel level between the probe and gallery due to the fact that there are generally too many variations and nuisances within raw face images. Instead, high-level features from face images may be extracted (e.g., as a representation or template of the subject's face) through either conventional methods, such as HOG, SIFT, etc., or a more advanced and data driven neural network approach, such as Dlib, Arcface, etc. The verification can then be conducted among, for example, the templates (e.g., face feature vectors) using similarity metrics such as Euclidean distance or cosine similarity.

Additionally or alternatively, ACS 100 may include a facial recognition module 107 that is external to reader 102, located within a vicinity (e.g., 20 meters) of the reader and/or secure asset 104. Facial recognition module 107 may comprise one or more components for providing the facial recognition or facial verification capabilities. For example, facial recognition module 107 may include one or more cameras or other optical sensors for capturing or receiving one or more images, such as one or more images of a user 101. Facial recognition module 107 may also include one or more processors and memory for performing facial recognition or facial verification using the captured or received images. Facial recognition module 107 may be operably connected by wire or wirelessly with reader 102, and may alternatively or additionally utilize one or more processors and/or memory of the reader 102.

In some cases, reader 102 can be connected by wire or wirelessly to a control panel 108. In such cases, reader 102 may transmit credential information to control panel 108, and the control panel may make, or may share responsibilities with the reader in making, access control determinations. Based on the access control determinations, control panel 108 can instruct reader 102 to operate or command control mechanism 106, accordingly. Alternately, control panel 108 can be connected directly or wirelessly to control mechanism 106, and in such cases may directly operate or command the control mechanism, accordingly, bypassing reader 102.

In some cases, reader 102 and control panel 108, and even control mechanism 106, can be connected to a wired or wireless network 110 and communicate with each other, as described above, via the network. Example networks can include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, wireless data networks (e.g., networks based on the IEEE 802.11 family of standards known as Wi-Fi or the IEEE 802.16 family of standards known as WiMax), networks based on the IEEE 802.15.4 family of standards, and peer-to-peer (P2P) networks, among others. If ACS 100 is managed by a remote system, the ACS can include a host server 112 connected by wire or wirelessly to network 110 and that may communicate with reader 102 and/or control panel 108. In such cases, reader 102 can transmit credential information to host server 112 via network 110 or can transmit credential information to control panel 108, which can then transmit the credential information to the host server via the network. Host server 112 may make, or may share responsibilities with reader 102 and/or control panel 108 in making, access control determinations. Based on the access control determinations, host server 112 can instruct reader 102, directly or indirectly via control panel 108, to operate or command control mechanism 106, accordingly. Alternately, host server 112 can instruct control panel 108 to operate or command control mechanism 106, accordingly. In still another example, host server 112 can be connected via network 110 to control mechanism 106 and directly operate or command the control mechanism, accordingly, bypassing reader 102 and control panel 108.

Facial recognition module 107 may similarly be connected by wire or wirelessly to control panel 108 and may exchange information relating to facial verification or other information directly with the control panel. Likewise, facial recognition module 107 can be connected to a wired or wireless network 110 and may communicate with any of the reader 102, control panel 108, and host server 112, via the network. Any data, such as but not limited to, gallery images or templates, instructions, algorithms, and/or trained machine learning models may be stored at or distributed across any one or more of the reader 102, facial recognition module 103/107, controller 108, or host server 112. Likewise, facial recognition or verification may be performed at or across one or more of the reader 102, facial recognition module 103/107, controller 108, or host server 112.

FIG. 3 illustrates a block diagram schematic of various components of an example reader 102 or facial recognition module 107. In general, reader 102 and/or facial recognition module 107 can include one or more of a memory 302, a processor 304, one or more antennas 306, a communication module 308, a network interface device 310, a user interface 312, a facial recognition module 313, and a power source or supply 314. While reader 102 and facial recognition module 107 are illustrated in FIG. 2 as devices affixed to a surface, for example a wall, reader 102 and/or facial recognition module 107 may also be a free-standing device or a portable device, such as but not limited to a mobile device. Moreover, in some example embodiments, such as but not limited to certain LACS embodiments, reader 102 and/or facial recognition module 107 may be a mobile device of the user, wherein, for example, the user may be attempting to access a logical asset via the user's own mobile device.

Memory 302 can be used in connection with the execution of application programming or instructions by processor 304, and for the temporary or long-term storage of program instructions or instruction sets 316 and/or credential or authorization data 318, such as credential data, credential authorization data, access control data or instructions, or facial recognition or verification data or instructions. For example, memory 302 can contain executable instructions 316 that are used by the processor 304 to run other components of reader 102 and/or to make access determinations based on credential or authorization data 318. Memory 302 can comprise a computer readable medium that can be any medium that can contain, store, communicate, or transport data, program code, or instructions for use by or in connection with reader 102. The computer readable medium can be, for example but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples of suitable computer readable medium include, but are not limited to, an electrical connection having one or more wires or a tangible storage medium such as a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or EEPROM), Dynamic RAM (DRAM), any solid-state storage device, in general, a compact disc read-only memory (CD-ROM), or other optical or magnetic storage device. Computer-readable media includes, but is not to be confused with, computer-readable storage medium, which is intended to cover all physical, non-transitory, or similar embodiments of computer-readable media.

Processor 304 can correspond to one or more computer processing devices or resources. For instance, processor 304 can be provided as silicon, as a Field Programmable Gate Array (FPGA), an Application-Specific Integrated Circuit (ASIC), any other type of Integrated Circuit (IC) chip, a collection of IC chips, or the like. As a more specific example, processor 304 can be provided as a microprocessor, Central Processing Unit (CPU), or plurality of microprocessors or CPUs that are configured to execute instructions sets stored in an internal memory 320 and/or memory 302.

Antenna 306 can correspond to one or multiple antennas and can be configured to provide for wireless communications between reader 102 and/or facial recognition module 107 and a credential or key device. Antenna(s) 306 can be arranged to operate using one or more wireless communication protocols and operating frequencies including, but not limited to, the IEEE 802.15.1, Bluetooth, Bluetooth Low Energy (BLE), near field communications (NFC), ZigBee, GSM, CDMA, Wi-Fi, RF, UWB, and the like. By way of example, antenna(s) 306 can be RF antenna(s), and as such, may transmit/receive RF signals through free-space to be received/transferred by a credential or key device having an RF transceiver.

Communication module 308 can be configured to communicate according to any suitable communications protocol with one or more different systems or devices either remote or local to reader 102 and/or facial recognition module 107, such as one or more control mechanisms 106 or control panel 108.

Network interface device 310 includes hardware to facilitate communications with other devices, such as control panel 108 or host server 112, over a communication network, such as network 110, utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks can include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, wireless data networks (e.g., networks based on the IEEE 802.11 family of standards known as Wi-Fi or the IEEE 802.16 family of standards known as WiMax), networks based on the IEEE 802.15.4 family of standards, and peer-to-peer (P2P) networks, among others. In some examples, network interface device 310 can include an Ethernet port or other physical jack, a Wi-Fi card, a Network Interface Card (NIC), a cellular interface (e.g., antenna, filters, and associated circuitry), or the like. In some examples, network interface device 310 can include one or more antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques.

User interface 312 can include one or more input devices and/or output devices. Examples of suitable user input devices that can be included in user interface 312 include, without limitation, one or more buttons, a keyboard, a mouse, a touch-sensitive surface, a stylus, a camera, a microphone, a PIN pad, touch screen, fingerprint reader, magnetic stripe reader, chip reader, etc. Examples of suitable user output devices that can be included in user interface 312 include, without limitation, one or more LEDs, a LCD panel, a display screen, a touchscreen, one or more lights, a speaker, etc. It should be appreciated that user interface 312 can also include a combined user input and user output device, such as a touch-sensitive display or the like.

Facial recognition module 313 may include one or more cameras or other optical sensors for capturing or receiving one or more images, such as one or more images of a user 101. Alternatively or additionally, facial recognition module 313 may utilize one or more cameras, if provided, of user interface 312. Facial recognition module 313 may also include its own processor or processors and/or memory for performing facial recognition or facial verification using the captured or received images. As noted above, facial recognition module 313 may alternatively or additionally utilize one or more processors 304 and/or memory 302 of the reader 102 for performing some or all of the facial recognition or facial verification. The memory of facial recognition module 313 (or reader 102) may, for example, store one or more gallery images or other representations of images, such as templates. The memory of facial recognition module 313 (or reader 102) may additionally or alternatively store instructions, algorithms, and/or one or more trained machine learning models for performing facial recognition or verification.

Power source 314 can be any suitable internal power source, such as a battery, capacitive power source or similar type of charge-storage device, etc., and/or can include one or more power conversion circuits suitable to convert external power into suitable power (e.g., conversion of externally-supplied AC power into DC power) for components of the reader 102 and/or facial recognition module 107. Power source 314 can also include some implementation of surge protection circuitry to protect the components of reader 102 and/or facial recognition module 107 from power surges.

Reader 102 and/or facial recognition module 107 can also include one or more interlinks or buses 322 operable to transmit communications between the various hardware components of the reader. A system bus 322 can be any of several types of commercially available bus structures or bus architectures.

FIG. 4 illustrates a block diagram schematic of various example components of an example machine 400 that can be used as, for example, control panel 108 and/or host server 112. Examples, as described herein, can include, or can operate by, logic or a number of components, modules, or mechanisms in machine 400. Modules may be hardware, software, or firmware communicatively coupled to one or more processors in order to carry out the operations described herein. Generally, circuitry (e.g., processing circuitry) is a collection of circuits implemented in tangible entities of machine 400 that include hardware (e.g., simple circuits, gates, logic, etc.). Circuitry membership can be flexible over time. Circuitries include members that can, alone or in combination, perform specified operations when operating. In some examples, hardware of the circuitry can be immutably designed to carry out a specific operation (e.g., hardwired). In some examples, the hardware of the circuitry can include variably connected physical components (e.g., execution units, transistors, simple circuits, etc.) including a machine readable medium physically modified (e.g., magnetically, electrically, moveable placement of invariant massed particles, etc.) to encode instructions of the specific operation. In connecting the physical components, the underlying electrical properties of a hardware constituent are changed, for example, from an insulator to a conductor or vice versa. The instructions permit embedded hardware (e.g., the execution units or a loading mechanism) to create members of the circuitry in hardware via the variable connections to carry out portions of the specific operation when in operation. Accordingly, in some examples, the machine readable medium elements are part of the circuitry or are communicatively coupled to the other components of the circuitry when the device is operating. In some examples, any of the physical components can be used in more than one member of more than one circuitry. For example, under operation, execution units can be used in a first circuit of a first circuitry at one point in time and reused by a second circuit in the first circuitry, or by a third circuit in a second circuitry at a different time. Additional and/or more specific examples of components with respect to machine 400 follow.

In some embodiments, machine 400 can operate as a standalone device or can be connected (e.g., networked) to other machines. In a networked deployment, machine 400 can operate in the capacity of a server machine, a client machine, or both in server-client network environments. In some examples, machine 400 can act as a peer machine in a peer-to-peer (P2P) (or other distributed) network environment. Machine 400 can be or include a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein, such as cloud computing, software as a service (SaaS), other computer cluster configurations.

Machine (e.g., computer system) 400 can include a hardware processor 402 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof) and a main memory 404, a static memory (e.g., memory or storage for firmware, microcode, a basic-input-output (BIOS), unified extensible firmware interface (UEFI), etc.) 406, and/or mass storage 408 (e.g., hard drives, tape drives, flash storage, or other block devices) some or all of which can communicate with each other via an interlink (e.g., bus) 430. Machine 400 can further include a display device 410 and an input device 412 and/or a user interface (UI) navigation device 414. Example input devices and UI navigation devices include, without limitation, one or more buttons, a keyboard, a touch-sensitive surface, a stylus, a camera, a microphone, etc.). In some examples, one or more of the display device 410, input device 412, and UI navigation device 414 can be a combined unit, such as a touch screen display. Machine 400 can additionally include a signal generation device 418 (e.g., a speaker), a network interface device 420, and one or more sensors 416, such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensor. Machine 400 can include an output controller 428, such as a serial (e.g., universal serial bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), NFC, etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.).

Processor 402 can correspond to one or more computer processing devices or resources. For instance, processor 402 can be provided as silicon, as a Field Programmable Gate Array (FPGA), an Application-Specific Integrated Circuit (ASIC), any other type of Integrated Circuit (IC) chip, a collection of IC chips, or the like. As a more specific example, processor 402 can be provided as a microprocessor, Central Processing Unit (CPU), or plurality of microprocessors or CPUs that are configured to execute instructions sets stored in an internal memory 422 and/or memory 404, 406, 408.

Any of memory 404, 406, and 408 can be used in connection with the execution of application programming or instructions by processor 402 for performing any of the functionality or methods described herein, and for the temporary or long-term storage of program instructions or instruction sets 424 and/or other data for performing any of the functionality or methods described herein. Any of memory 404, 406, 408 can comprise a computer readable medium that can be any medium that can contain, store, communicate, or transport data, program code, or instructions 424 for use by or in connection with machine 400. The computer readable medium can be, for example but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples of suitable computer readable medium include, but are not limited to, an electrical connection having one or more wires or a tangible storage medium such as a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or EEPROM), Dynamic RAM (DRAM), a solid-state storage device, in general, a compact disc read-only memory (CD-ROM), or other optical or magnetic storage device. As noted above, computer-readable media includes, but is not to be confused with, computer-readable storage medium, which is intended to cover all physical, non-transitory, or similar embodiments of computer-readable media.

Network interface device 420 includes hardware to facilitate communications with other devices over a communication network, such as network 110, utilizing any one of a number of transfer protocols (e.g., frame relay, internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks can include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, wireless data networks (e.g., networks based on the IEEE 802.11 family of standards known as Wi-Fi or the IEEE 802.16 family of standards known as WiMax), networks based on the IEEE 802.15.4 family of standards, and peer-to-peer (P2P) networks, among others. In some examples, network interface device 420 can include an Ethernet port or other physical jack, a Wi-Fi card, a Network Interface Card (NIC), a cellular interface (e.g., antenna, filters, and associated circuitry), or the like. In some examples, network interface device 420 can include one or more antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques.

As indicated above, machine 400 can include one or more interlinks or buses 430 operable to transmit communications between the various hardware components of the machine. A system bus 322 can be any of several types of commercially available bus structures or bus architectures.

With reference back to FIGS. 1 and 2, in use, as a user 101 having a credential or key device 114 (illustrated, for example, as a smartcard 114 a or mobile device 114 b) approaches reader 102 associated with access point 105, the credential device 114 may communicate the user's credential or credential data to the reader, for example, via a suitable RFID or PAN technology. In general, a credential device 114 may include any device that carries evidence of authority, status, rights, and/or entitlement to privileges for a holder of the credential device. A credential device 114 can be a portable device having memory 116, storing one or more user credentials or credential data, and a reader interface (i.e., an antenna and Integrated Circuit (IC) chip) 118, which permits the credential to exchange data with a reader device, such as reader 102, via a credential interface of the reader device, such as antenna 306. One example of credential device 114 is an RFID smartcard (e.g., smartcard 114 a) that has data stored thereon allowing a holder of the credential device to access a secure area or asset protected by reader 102, such as secure area 104. Other examples of credential devices 114 include, but are not limited to, proximity RFID-based cards, access control cards, credit cards, debit cards, passports, identification cards, key fobs, NFC-enabled devices, mobile phones (e.g., mobile device 114 b), personal digital assistants (PDAs), tags, or any other device configurable to emulate a virtual credential. In some example embodiments, such as but not limited to certain LACS embodiments, reader 102 and/or facial recognition module 103/107 and credential device 114 may be the same device, wherein, for example, the user may be attempting to access a logical asset via the user's own mobile device (e.g., mobile device 114 b).

If reader 102, control panel 108, and/or host server 112 determine that the user's 101 credential or credential data provided by credential device 114 is valid and/or authorized, reader 102, control panel 108, or host server 112 may operate control mechanism 106 to allow access to the secure asset 104 by the user 101 having the credential device. In some embodiments, facial recognition may be used as a second authentication factor, authentication of which may be required before reader 102, control panel 108, and/or host server 112 operate control mechanism 106 to allow access to the secure asset 104 by the user 101 having the credential device. In other example embodiments, facial recognition may be used as a sole authentication factor, and thus, credential data from the user's credential device 114 need not be provided or authenticated.

Facial recognition systems generally require a user 101 to have one or more verified pictures of their face captured (e.g., an enrollment photo), such as during an enrollment step or process. Such enrollment may be completed using a device operably connected by wire or wirelessly to ACS 100, such as to the host server 112. Such device may be, or may be similar to, any of the devices described herein, such as the reader 102, facial recognition module 107, controller 108, credential device 114 b, or the like. ACS 100 may use the enrollment photo(s) to develop one or more representations of the user's 101 face, such as one or more facial templates, that generally represent various characteristics of the user's face, such as but not limited to, the spacing between two or more facial elements, the size of one or more facial elements, the aspect ratio of one or more facial elements, the shape of one or more facial elements, the position relative within the face of one or more facial elements, etc.

Once a facial template (or other representation) has been created, it may be distributed to a variety of devices throughout ACS 100, such as one or more host servers 112, one or more controllers 108, and/or one or more readers 106. Subsequently, when an image or video feed (e.g., from which images can be obtained) of the user 101 is captured by reader 102 or facial recognition module 103/107, the facial recognition module 103/107, alone or in conjunction with reader 102, controller 108, and/or host server 112, can create a substantially real-time representation or model (e.g., facial template) of the user's face using one or more images captured by reader 102 or facial recognition module 103/107 and may search through facial representations or templates of a plurality of enrolled users previously distributed (e.g., the gallery) and determine whether there is a match. Facial recognition module 103/107, alone or in conjunction with reader 102, controller 108, and/or host server 112, may take advantage of a deep neural network that has been trained to analyze faces and search a database of facial representations or templates (e.g., a gallery) to determine matches.

Facial recognition module 103/107, alone or in conjunction with reader 102, controller 108, and/or host server 112, may return one or more potential or probable matches for the image of the user 101 captured by reader 102 or facial recognition module 103/107, along with a “confidence” value associated with each match. The confidence value may be affected by many factors, such as but not limited to, the quality of the representation or template generated from the enrollment photo, the quality of the image or video feed captured by reader 102 or facial recognition module 103/107, ambient lighting, age or aging of the user, and changes in appearance of the user, such as due to changes in facial hair, addition or removal of glasses, etc. The facial recognition module 103/107, alone or in conjunction with reader 102, controller 108, and/or host server 112, may determine whether the confidence value of a match meets or exceeds a certain, possibly predefined, threshold for authentication, and if so, then the ACS 100, such as via reader 102, facial recognition module 103/107, controller 108, and/or a host server 112, may permit access to the secure asset 104 by the user of the credential device 114.

In an example embodiment, multiple images or other representations of images (e.g., facial templates) (also referred to herein as multiple profiles) for each user may be stored and/or distributed throughout ACS 100, as described above. Each of the multiple images or other representations of images (the multiple profiles) may be associated with a different appearance of the user, such as but not limited to, changes in appearance resulting from facial hair, the addition or removal of glasses, a change in hair style, cosmetic procedures, different lighting, etc. In other example embodiments, such multiple images or other representations of images (e.g., facial templates) for a given user may be stored on the user's credential device 114, as described in the copending application titled “FACIAL RECOGNITION TEMPLATE STORED ON MOBILE CREDENTIAL,” having attorney docket number 5483.620PRV, which is hereby incorporated by reference in its entirety. More specifically, in an example embodiment, multiple facial templates (or other representations) of a user 101 generated from one or more enrollment photos of the user may be generated by, or distributed to, and stored directly on the user's credential device 114. As the user 101 approaches reader 102 and/or facial recognition module 103/107, the user's credential device 114 may establish a secure connection with the reader and/or facial recognition module to transmit one or more of the user's facial templates (or other representations) stored on the credential device to the reader and/or facial recognition module, for example, via a suitable RFID or PAN technology. This may be done independently or in connection with the communication from the credential device 114 to the reader 102 and/or facial recognition module 103/107 of the user's credential or credential data, as discussed above. At least one template or other representation, and in an example embodiment two or more templates/representations, transmitted from the credential device 114 to the reader 102 and/or facial recognition module 103/107 may then be used as the gallery image(s) for comparison against the template or other representation generated from the probe image of the user 101 captured in substantially real-time by the reader and/or facial recognition module, as described above.

In another example embodiment, multiple images (e.g., conventional user or non-templated images, such as but not limited to, a “normal” photograph or JPG image) of a user 101, not dissimilar from the type of image used as an enrollment photo, may be captured by, or distributed to, and stored directly on the user's credential device 114. As the user 101 approaches reader 102 and/or facial recognition module 103/107, the user's credential device 114 may establish a secure connection with the reader and/or facial recognition module to transmit one or more of the images stored on the credential device to the reader and/or facial recognition module, for example, via a suitable RFID or PAN technology. This may be done independently or in connection with the communication from the credential device 114 to the reader 102 and/or facial recognition module 103/107 of the user's credential or credential data, as discussed above. The reader 102 and/or facial recognition module 103/107, alone or in conjunction with the controller 108 and/or host server 112, can create one or more substantially real-time representations or models (e.g., facial templates) of the user's face using the one or more, and in an example embodiment two or more, images received from the user's 101 credential device 114. The representation(s) or template(s) generated using the one or more images received from the user's 101 credential device 114 may then be used as the gallery image(s) for comparison against the template or other representation generated from the probe image of the user 101 captured in substantially real-time by the reader and/or facial recognition module, as described above.

With such multiple profile approach, however, there can be some issues. For example, one problem with this approach is that while such images or other representations of images (e.g., facial templates) are each generally considered relatively small in size by conventional computer memory standards, they are not considered small in terms of memory capabilities of certain credential devices, such as proximity or smart cards, fobs, or similar token devices.

Accordingly, in an example embodiment, for two images or two other representations of images (e.g., templates) of a user's multiple profiles, a difference model or template may be generated that is generally indicative of just the differences between the images or templates. For example, one image might include a photograph of the user with no facial hair while a second image might be a photograph of the user in which facial hair has been added, sunglasses have been added, and/or some other cosmetic change has been made. As another example, one template (or other representation of an image) might be generated from an image of the user with no facial hair while a second template (or other representation of an image) might be generated from an image of the user in which facial hair has been added, sunglasses have been added, and/or some other cosmetic change has been made. A difference model or template may be generated from these first and second images or first and second templates and may be indicative generally of just the changes from the first image/template to the second image/template. Rather than storing a full second image, for example, the difference model may be stored, such as in the form of a “layer,” that could be applied to the original/first image or template during facial recognition or verification, described above. The difference model or template will generally take up less space in memory than a full second image/template.

The above example embodiments provide several advantages, including but not limited to:

(1) User authentication can be at least bimodal (e.g., dual factor), including authentication of the users' credential or credential data and authentication of the user's facial template (e.g., facial recognition), wherein pursuant to the above example embodiments, the credential and facial template are bound together on the credential device 114.

(2) As facial recognition systems continue to improve and new models are built, such systems can be upgraded at generally a single point. New facial models/templates in a database or distributed across an ACS do not need to be updated, as there is no database of facial templates to update. Any new algorithm(s) can simply be pushed out to the facial recognition components of the ACS and all users would immediately be using the latest technology without needing to update facial templates.

(3) Facial recognition data stored on the credential device 114 becomes usable in different systems as long as they can read, or exchange date with, the credential device. That is, for example, facial recognition by even radically different facial recognition systems may be completed based on the same information (e.g., user image or template) stored on the credential device 114, essentially creating a standard credential that may be used across systems.

(4) Facial recognition systems do not need to be “online” or network-connected in order to add users to the system, as distribution of user gallery images/templates is not necessary.

(5) ACS 100 is inherently more secure because two-factor authentication can be used.

(6) The threshold that the confidence value of a “match” must meet for authentication can be significantly reduced. This avoids problems arising from a significantly high threshold value that might otherwise be required in an ACS. For example, no matter which alphanumerical system (e.g., a numerical range, such as 1-10, 1-100, or other suitable range, an alphabetical range, such as from A-F or other alphabetical range, etc.) or other system that is used to represent the confidence value, the threshold that the confidence value must meet in order to qualify as a “match,” in some embodiments, may be predefined, predetermined, or selected so as to correspond to a threshold value that, under that system, equates to a threshold falling somewhere in the range of 60% to 70% (inclusive), a threshold value falling somewhere in the range of 70% to 80% (inclusive), a threshold value falling somewhere in the range of 80% to 90% (inclusive), or a threshold value falling somewhere above 90%. However, a threshold value that equates to a threshold falling in any other suitable range (such as less than 60% or greater than 90%) may also be used.

(7) Storing, for example, a base image/template and one or more difference models or templates, as described above, generally utilizes a relatively small footprint, and in some cases a much smaller footprint as compared to storing a plurality of full images/templates. This can be particular desirable for certain credentials with relatively low memory capabilities, such as proximity or smart cards, fobs, or similar token devices.

FIG. 5 illustrates a method 500, in an ACS, for permitting or denying access to a secure asset. At step 502, during an enrollment process, for example, a user has a plurality of verified pictures of their face captured (e.g., an enrollment photo). From the pictures, a base image and one or more difference models/templates, each indicative, for example, of the difference(s) between another one of the pictures and the base image, may be generated and stored directly on a credential device of the user. Additionally or alternatively, from the pictures, a base template or other representation of an image may be generated and it, along with one or more difference models/templates, each indicative, for example, of the difference(s) between a template generated from another one of the pictures and the base template, may be generated and stored directly on a credential device of the user. At step 504, as the user approaches a reader and/or facial recognition module (“reader/FR module”) of the ACS, the user's credential device may communicate the stored base image or template and at least one of the stored difference models/templates to the reader/FR module, for example, via a suitable RFID or PAN technology. Also, before, at the same time, or after the base image/template and difference model(s)/template(s) are communicated to the reader/FR module, the user's credential device may communicate the user's credential or credential data, if any, to the reader/FR module, via the suitable RFID or PAN technology, for example. As mentioned above, in some example embodiments, such as but not limited to certain LACS embodiments, the reader/FR module and the user's credential device may be the same device, wherein, for example, the user may be attempting to access a logical asset, such as but not limited to, a financial or personal account, via the user's own mobile device. In such cases, the reader/FR module (i.e., the user's mobile device) may already contain the base image/template and difference model(s)/template(s) and any credential or credential data of the user. At step 506, if a base image (e.g., non-templated image) is communicated from the user's credential device to the reader/FR module in step 504, then the reader/FR module, alone or in conjunction with the ACS controller and/or host server, can generate at least one substantially real-time representation or model (e.g., facial template) of the user's face using the base image received from the user's credential device. At step 508, which may occur before, simultaneous with, or after steps 504 and 506, the reader/FR module captures at least one probe image of the user and, alone or in conjunction with the ACS controller and/or host server, can generate at least one substantially real-time representation or model (e.g., facial template) of the user's face using the captured probe image(s). At step 510, the base template received from the user's credential device at step 504 or the template generated by the reader/FR module using the base image received from the user's credential device at step 506, either alone or combined with one or more of the difference models/templates, may then be used by the reader/FR module, alone or in conjunction with the ACS controller and/or host server, as a gallery image for comparison against a template or other representation generated from the probe image(s) of the user captured at step 508 to determine the likelihood or confidence of a match. At step 512, which can occur any time after the reader receives a credential or credential data, if any, for example at step 504, the reader/FR module, alone or in conjunction with the ACS controller and/or host server, may validate the credential or credential data. At step 514, if the reader/FR module, alone or in conjunction with the ACS controller and/or host server, determines a likelihood or confidence of a match at step 510 that meets or exceeds a certain, possibly predefined, threshold, and in cases where a credential or credential data has been communicated by the credential device and is also required by the ACS for access to the secure asset, if the reader/FR module, alone or in conjunction with the ACS controller and/or host server, determines that such credential or credential data is valid, then the ACS, such as via the reader/FR module, controller, and/or a host server, may permit access to the secure asset by the user of the credential device.

Although the flowchart of FIG. 5 illustrates an example method as comprising sequential steps or a process as having a particular order of operations, many of the steps or operations in the flowchart can be performed in parallel or concurrently, and the flowchart should be read in the context of the various embodiments of the present disclosure. The order of the method steps or process operations illustrated in FIG. 5 may be rearranged for some embodiments. Similarly, the method illustrated in FIG. 5 could have additional steps or operations not included therein or fewer steps or operations than those shown.

ADDITIONAL EXAMPLES

Example 1 includes subject matter relating to a non-transitory computer readable medium comprising executable program code, that when executed by one or more processors, causes the one or more processors to: receive, at a reader device from a credential device of a user, a first representation of a face of the user generated from a first image of the user and receive, at the reader device from the credential device, a difference model, the difference model indicative of a difference between the first image and a second image of the user; capture, by the reader device, a third image of the user and generate a second representation of the face of the user using the third image; at least one of determine a first likelihood of a match between the first and second representations or determine a second likelihood of a match between the second representation and a third representation, the third representation being a combination of the first representation and the difference model; and permit access by the user to a secure asset in instances where at least one of the first or second likelihoods of a match meets a threshold.

In Example 2, the subject matter of Example 1 optionally includes wherein a data size of the first representation and the difference model is less than a data size of the first image and the second image.

In Example 3, the subject matter of Example 1 or 2 optionally includes wherein a data size of the first representation and the difference model is less than a data size of the first representation and a fourth representation of the face of the user using the second image.

In Example 4, the subject matter of any of Examples 1 to 3 optionally includes wherein the first image and second image are each associated with a different appearance of the user.

In Example 5, the subject matter of any of Examples 1 to 4 optionally includes wherein the executable code, when executed by the one or more processors, causes the one or more processors to further: receive, at the reader device from the credential device of the user, credential data; and validate the credential data.

In Example 6, the subject matter of Example 5 optionally includes wherein permitting access by the user to the secure asset comprises permitting access by the user to the secure asset in instances where both the credential data is valid and at least one of the first or second likelihoods of a match meets the threshold.

In Example 7, the subject matter of any of Examples 1 to 6 optionally includes wherein the executable code, when executed by the one or more processors, causes the one or more processors to further establish a secure wireless connection for receiving the first representation and the difference model.

Example 8 includes subject matter relating to a non-transitory computer readable medium comprising executable program code, that when executed by one or more processors, causes the one or more processors to: receive, at a reader device from a credential device of a user, a first representation of a face of the user generated from a first image of the user and receive, at the reader device from the credential device, a plurality of difference models, each of the difference models being indicative of a difference between the first image and at least one of a plurality of other images of the user; capture, by the reader device, a third image of the user and generate a second representation of the face of the user using the third image; at least one of determine a first likelihood of a match between the first and second representations or determine a second likelihood of a match between the second representation and any of a plurality of third representations, each third representation being a combination of the first representation and one or more of the difference models; and permit access by the user to a secure asset in instances where at least one of the first or second likelihoods of a match meets a threshold.

In Example 9, the subject matter of Example 8 optionally includes wherein a data size of the first representation and the difference models is less than a data size of the first representation and a plurality of representations of the face of the user based on the plurality of other images.

In Example 10, the subject matter of Example 8 or 9 optionally includes wherein the first image and each of the plurality of other images are each associated with a different appearance of the user.

In Example 11, the subject matter of any of Examples 8 to 10 optionally includes wherein: the executable code, when executed by the one or more processors, causes the one or more processors to further: receive, at the reader device from the credential device of the user, credential data; and validate the credential data; and wherein permitting access by the user to the secure asset comprises permitting access by the user to the secure asset in instances where both the credential data is valid and at least one of the first or second likelihoods of a match meets the threshold.

Example 12 includes subject matter relating to a device for permitting access to a secure asset, the device comprising: at least one processor; and memory storing instructions that when executed by the at least one processor cause the at least one processor to: receive a first representation of a face of a user from a credential device of the user, the first representation generated from a first image of the user; receive a difference model from the credential device of the user, the difference model indicative of a difference between the first image and a second image of the user; capture a third image of the user and generate a second representation of the face of the user using the third image; at least one of determine a first likelihood of a match between the first and second representations or determine a second likelihood of a match between the second representation and a third representation, the third representation being a combination of the first representation and the difference model; and permit access by the user to the secure asset in instances where at least one of the first or second likelihoods of a match meets a threshold.

In Example 13, the subject matter of Example 12 optionally includes wherein a data size of the first representation and the difference model is less than a data size of the first representation and a fourth representation of the face of the user using the second image.

In Example 14, the subject matter of Example 12 or 13 optionally includes wherein the secure asset is a physical asset.

In Example 15, the subject matter of Example 12 or 13 optionally includes wherein the secure asset is a logical asset.

In Example 16, the subject matter of any of Examples 12 to 15 optionally includes wherein the device comprises a reader device connected with a facial recognition module that is external to the reader device, the facial recognition module comprising an optical sensor for capturing the third image of the user.

In Example 17, the subject matter of Example 16 optionally includes wherein the memory storing instructions is divided between the reader device and the facial recognition module.

In Example 18, the subject matter of any of Examples 12 to 17 optionally includes wherein the instructions, when executed by the at least one processor, cause the at least one processor to further: receive credential data from the credential device; and validate the credential data.

In Example 19, the subject matter of Example 18 optionally includes wherein permitting access by the user to the secure asset comprises permitting access by the user to the secure asset in instances where both the credential data is valid and at least one of the first or second likelihoods of a match meets the threshold.

In Example 20, the subject matter of Example 19 optionally includes wherein the device comprises a reader device connected with a facial recognition module that is external to the reader device, wherein: the reader device is configured with the instructions that cause the at least one processor to receive credential data from the credential device and validate the credential data; and the facial recognition module comprises an optical sensor and is configured with the instructions for capturing the third image of the user.

ADDITIONAL NOTES

The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments that can be practiced. These embodiments may also be referred to herein as “examples.” Such embodiments or examples can include elements in addition to those shown or described. However, the present inventors also contemplate examples in which only those elements shown or described are provided. Moreover, the present inventors also contemplate examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein. That is, the above-described embodiments or examples or one or more aspects, features, or elements thereof can be used in combination with each other.

As will be appreciated by one of skill in the art, the various embodiments of the present disclosure may be embodied as a method (including, for example, a computer-implemented process, a business process, and/or any other process), apparatus (including, for example, a system, machine, device, computer program product, and/or the like), or a combination of the foregoing. Accordingly, embodiments of the present disclosure or portions thereof may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, middleware, microcode, hardware description languages, etc.), or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present disclosure may take the form of a computer program product on a computer-readable medium or computer-readable storage medium, having computer-executable program code embodied in the medium, that define processes or methods described herein. A processor or processors may perform the necessary tasks defined by the computer-executable program code. In the context of this disclosure, a computer readable medium may be any medium that can contain, store, communicate, or transport the program for use by or in connection with the systems disclosed herein. As indicated above, the computer readable medium may be, for example but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples of suitable computer readable medium include, but are not limited to, an electrical connection having one or more wires or a tangible storage medium such as a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or EEPROM), a compact disc read-only memory (CD-ROM), or other optical, magnetic, or solid state storage device. As noted above, computer-readable media includes, but is not to be confused with, computer-readable storage medium, which is intended to cover all physical, non-transitory, or similar embodiments of computer-readable media.

As used herein, the terms “substantially” or “generally” refer to the complete or nearly complete extent or degree of an action, characteristic, property, state, structure, item, or result. For example, an object that is “substantially” or “generally” enclosed would mean that the object is either completely enclosed or nearly completely enclosed. The exact allowable degree of deviation from absolute completeness may in some cases depend on the specific context. However, generally speaking, the nearness of completion will be so as to have generally the same overall result as if absolute and total completion were obtained. The use of “substantially” or “generally” is equally applicable when used in a negative connotation to refer to the complete or near complete lack of an action, characteristic, property, state, structure, item, or result. For example, an element, combination, embodiment, or composition that is “substantially free of” or “generally free of” an element may still actually contain such element as long as there is generally no significant effect thereof.

In the foregoing description various embodiments of the present disclosure have been presented for the purpose of illustration and description. They are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obvious modifications or variations are possible in light of the above teachings. The various embodiments were chosen and described to provide the best illustration of the principals of the disclosure and their practical application, and to enable one of ordinary skill in the art to utilize the various embodiments with various modifications as are suited to the particular use contemplated. All such modifications and variations are within the scope of the present disclosure as determined by the appended claims when interpreted in accordance with the breadth they are fairly, legally, and equitably entitled. 

What is claimed is:
 1. A non-transitory computer readable medium comprising executable code, that when executed by one or more processors, causes the one or more processors to: receive, at a reader device from a credential device of a user, a first representation of a face of the user generated from a first image of the user and receive, at the reader device from the credential device, a difference model, the difference model indicative of a difference between the first image and a second image of the user; capture, by the reader device, a third image of the user and generate a second representation of the face of the user using the third image; at least one of determine a first likelihood of a match between the first and second representations or determine a second likelihood of a match between the second representation and a third representation, the third representation being a combination of the first representation and the difference model; and permit access by the user to a secure asset in instances where at least one of the first or second likelihoods of a match meets a threshold.
 2. The non-transitory computer readable medium of claim 1, wherein a data size of the first representation and the difference model is less than a data size of the first image and the second image.
 3. The non-transitory computer readable medium of claim 1, wherein a data size of the first representation and the difference model is less than a data size of the first representation and a fourth representation of the face of the user using the second image.
 4. The non-transitory computer readable medium of claim 1, wherein the first image and second image are each associated with a different appearance of the user.
 5. The non-transitory computer readable medium of claim 1, wherein the executable code, when executed by the one or more processors, causes the one or more processors to further: receive, at the reader device from the credential device of the user, credential data; and validate the credential data.
 6. The non-transitory computer readable medium of claim 5, wherein permitting access by the user to the secure asset comprises permitting access by the user to the secure asset in instances where both the credential data is valid and at least one of the first or second likelihoods of a match meets the threshold.
 7. The non-transitory computer readable medium of claim 1, wherein the executable code, when executed by the one or more processors, causes the one or more processors to further establish a secure wireless connection for receiving the first representation and the difference model.
 8. A non-transitory computer readable medium comprising executable code, that when executed by one or more processors, causes the one or more processors to: receive, at a reader device from a credential device of a user, a first representation of a face of the user generated from a first image of the user and receive, at the reader device from the credential device, a plurality of difference models, each of the difference models being indicative of a difference between the first image and at least one of a plurality of other images of the user; capture, by the reader device, a third image of the user and generate a second representation of the face of the user using the third image; at least one of determine a first likelihood of a match between the first and second representations or determine a second likelihood of a match between the second representation and any of a plurality of third representations, each third representation being a combination of the first representation and one or more of the difference models; and permit access by the user to a secure asset in instances where at least one of the first or second likelihoods of a match meets a threshold.
 9. The non-transitory computer readable medium of claim 8, wherein a data size of the first representation and the difference models is less than a data size of the first representation and a plurality of representations of the face of the user based on the plurality of other images.
 10. The non-transitory computer readable medium of claim 8, wherein the first image and each of the plurality of other images are each associated with a different appearance of the user.
 11. The non-transitory computer readable medium of claim 8, wherein: the executable code, when executed by the one or more processors, causes the one or more processors to further: receive, at the reader device from the credential device of the user, credential data; and validate the credential data; and wherein permitting access by the user to the secure asset comprises permitting access by the user to the secure asset in instances where both the credential data is valid and at least one of the first or second likelihoods of a match meets the threshold.
 12. A device for permitting access to a secure asset, the device comprising: at least one processor; and memory storing instructions that when executed by the at least one processor cause the at least one processor to: receive a first representation of a face of a user from a credential device of the user, the first representation generated from a first image of the user; receive a difference model from the credential device of the user, the difference model indicative of a difference between the first image and a second image of the user; capture a third image of the user and generate a second representation of the face of the user using the third image; at least one of determine a first likelihood of a match between the first and second representations or determine a second likelihood of a match between the second representation and a third representation, the third representation being a combination of the first representation and the difference model; and permit access by the user to the secure asset in instances where at least one of the first or second likelihoods of a match meets a threshold.
 13. The device of claim 12, wherein a data size of the first representation and the difference model is less than a data size of the first representation and a fourth representation of the face of the user using the second image.
 14. The device of claim 12, wherein the secure asset is a physical asset.
 15. The device of claim 12, wherein the secure asset is a logical asset.
 16. The device of claim 12, wherein the device comprises a reader device connected with a facial recognition module that is external to the reader device, the facial recognition module comprising an optical sensor for capturing the third image of the user.
 17. The device of claim 16, wherein the memory storing instructions is divided between the reader device and the facial recognition module.
 18. The device of claim 12, wherein the instructions, when executed by the at least one processor, cause the at least one processor to further: receive credential data from the credential device; and validate the credential data.
 19. The device of claim 18, wherein permitting access by the user to the secure asset comprises permitting access by the user to the secure asset in instances where both the credential data is valid and at least one of the first or second likelihoods of a match meets the threshold.
 20. The device of claim 19, wherein the device comprises a reader device connected with a facial recognition module that is external to the reader device, wherein: the reader device is configured with the instructions that cause the at least one processor to receive credential data from the credential device and validate the credential data; and the facial recognition module comprises an optical sensor and is configured with the instructions for capturing the third image of the user. 